Instrumentalness vs danceability

This plot shows the danceability and the instrumentalness of the national anthems around the world. The size of the data points indicates the level of energy. The color illustrates whether or not the data point is in Minor or Major. From this plot, the american continent has the most instrumentalness in their national anthems. The most danceable national anthem is in Asia, Kuwait has a danceability of 0.8760. What is also interesting is that within the americas playlist, the national anthems from English speaking countries all have less instrumentalness than the Spanish speaking countries which could mean that culture could have an influence on the national anthem instrumentalness.

Chordogram of the most danceable national anthem

Chordogram of the least danceable national anthem

***

These Chordograms show two outliers of the from spotify defined variable danceabelity. The national anthem of Kuwait is the most danceable anthem in the world. The chordogram of this anthem illustrates the different chords over time. A change of chords is visible around 8 seconds, where Bb:min loses intensity and F:maj starts to gain intensity. At around 21 seconds this changes back to where it was in the beginning. The second chordogram is the chordogram of Aruba, the least danceable anthem in the world. This chordogram shows changes at around 15 seconds, 50 seconds, 70 seconds and 85 seconds. What is different between these chordograms is that the least danceable song has more changes in chords than that the most danceable song has which looks like it uses more or less the same chords. It could be because of this difference in the amount of changes that Spotify characterizes songs as more danceable or less danceable.

Tempo

Timbre

The Tempo plot shows the standard deviation of the tempo variable from Spotify and its mean Tempo in beats per minute. From this plot it can be concluded that the mean tempo of the national anthems in Asia are the lowest. The standard Deviation of Europe seem to be the highest. The national anthems of the American continent have the longest duration.

In the second figure, the Timbre of the three continents are plotted. Overall, most of the timbre coefficients seem to be the same between the three continents. Coefficient c1, c2 and c3 however could be a marker that distinguishes the European continent from the rest with slight changes in its shape.

Energy vs Valence

This plots compare the Valence and Energy of each country within the playlists. For the american continent, most countries are clearly centered around 0.5 Valence. All the countries have an Energy of 0.5 and are in Major. The country that has the loudest national anthem is Canada with a loudness of 8.10e-5. The country national anthem with the highest Energy is that of the United States Virgin Islands with an energy of 0.48. In Asia, the national anthems are more spread out between the different values of Valence. Most countries have a Valence greater than 0.5. Compared between the other continents, Asia has the loudest national anthems where China has a loudness of 1.94e-4. Also, two countries have an Energy level above 0.5, Qatar with an Energy level of 0.5710 and Kuwait with a level of 0.5760. Europe has the most national anthems in Minor and it seems that most countries also have a national anthem with a Valence smaller than 0.5.

Timbre

Chroma

In this section, the chroma and timbre of the Chinese National Anthem are compared. The first plots show the timbre changes which are mostly present on the c3 level. When listening to the song the timbre seems to correlate with the sounds of the trumpets. At around 6 seconds there is a small dip visible and at around 21 seconds there is a gap which align with the sound of the trumpets in the song.

The chromagram shows that the song is mostly grounded by B, G, D. Like the cepstrogram that shows the timbre, at around 21 seconds there is a clear gap visible, where D gets a lower magnitude. The magnitude of G and B get bigger around that time.

The similarity graphs do not show any clear diagonal lines that could indicate repetitions which is different than expected because the song has many parts that are repeated.

Feature importance using random forest

Important features

Using the data of the Spotify API a random forest classifier is trained to classify the different national anthems into the three continents; Americas, Asia and Europe. The most important features are plotted above and show that Liveness, energy and size all have a high importance when classifying the different national anthems. The plot above shows these variables plotted against each other. On the x-axis Liveness is used and on the y-axis Energy. Each continent has its own color and the size of each data point represents the Speechiness. The plot illustrates that even with these features it will be difficult to classify the national anthems between the different continents because of the overlap between them. However, all the national anthems in Europe have a Liveness less than 0.3 which helps the algorithm to exclude songs above this value to categorize it in the Asian or American continent category.